code: def __init__(self, hidden_channels, num_layers, GNN=GCNConv, k=0.6):
      super().__init__()
  
      if k < 1:  # Transform percentile to number.
          num_nodes = sorted([data.num_nodes for data in train_dataset])
          k = num_nodes[int(math.ceil(k * len(num_nodes))) - 1]
          k = int(max(10, k))
  
      self.convs = ModuleList()
      self.convs.append(
          GNN(train_dataset.num_features, hidden_channels)
      )
      for i in range(0, num_layers - 1):
          self.convs.append(GNN(hidden_channels, hidden_channels))
      self.convs.append(GNN(hidden_channels, 1))
  
      conv1d_channels = [16, 32]
      total_latent_dim = hidden_channels * num_layers + 1
      conv1d_kws = [total_latent_dim, 5]
      self.conv1 = Conv1d(
          1, conv1d_channels[0], conv1d_kws[0], conv1d_kws[0]
      )
      self.pool = SortAggregation(k)
      self.maxpool1d = MaxPool1d(2, 2)
      self.conv2 = Conv1d(
          conv1d_channels[0], conv1d_channels[1], conv1d_kws[1], 1
      )
      dense_dim = int((k - 2) / 2 + 1)
      dense_dim = (dense_dim - conv1d_kws[1] + 1) * conv1d_channels[1]
      self.mlp = MLP([dense_dim, 128, 1], dropout=0.5, norm=None)
  
imports: import math
  import os.path
  import time
  from itertools import chain
  import numpy
  import torch
  import torch.nn.functional
  from scipy.sparse.csgraph import shortest_path
  from sklearn.metrics import roc_auc_score
  from torch.nn import BCEWithLogitsLoss, Conv1d, MaxPool1d, ModuleList
  from torch_geometric.data import Data, InMemoryDataset
  from torch_geometric.datasets import Planetoid
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn import MLP, GCNConv, SortAggregation
  from torch_geometric.transforms import RandomLinkSplit
  from torch_geometric.utils import k_hop_subgraph, to_scipy_sparse_matrix
label_desc: Use image node_id 1 to create a new DGCNN object from inherited base classes: torch.nn with example: obj = DGCNN(hidden_channels, num_layers, GNN, k)